^jdZddlZddlZddlZddlmZddlmZmZm Z m Z ddl m Z m Z mZmZmZddl Z ddlZee j(j+dddDcgc] }e| c}ZdZd Zed e Zee d rCe j:j<e j:j>fZ e j:j>Z!n$e j<e j>fZ e j>Z!d Z"dZ#dZ$dZ%dZ&dZ'dZ(dZ)dZ*dZ+dZ,dZ-dZ.dndZ/dZ0dZ1dZ2dZ3dZ4d Z5d!Z6d"Z7d#Z8d$Z9d%Z:d&Z;dod(Zd+Z?d,Z@d-ZAd.ZBd/ZCd0ZDd1ZEid2dd3dd4dd5e9d6e@d7eAd8eBd9eCd:eDd;eEde:d?e;d@e:dAe;dBe:e;e=e=e>e>e?e?eee1d?e1d@e3dAe3dBe4e4e$e%e(e)e&e'e7e5e6dG ZGGdHdIZHdJZIdKZJdLZKdMZLdNZMdpdOZNGdPdQZOdqdRePdSe efdTZQgdUZRgdVZSgdWZTdXdXdXdYdddZdZdZdZdZdZdZdZd[ZUdYdddZdZd\d\d\d]d]d]d]d]d]d]d^ZVd_efd`ZWdrdaePfdbZX dsdce eeYfddeYdSe ed_e e eeffdeZZGdfdgZ[ dtdRePdSe ed_e e ePeeffdhZ\gdiZ] dudce eeYfdSe ed_e e ePeeffdjZ^GdkdlZ_dqdRePdSe efdmZ`ycc}w)va AutoAugment, RandAugment, AugMix, and 3-Augment for PyTorch This code implements the searched ImageNet policies with various tweaks and improvements and does not include any of the search code. AA and RA Implementation adapted from: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py AugMix adapted from: https://github.com/google-research/augmix 3-Augment based on: https://github.com/facebookresearch/deit/blob/main/README_revenge.md Papers: AutoAugment: Learning Augmentation Policies from Data - https://arxiv.org/abs/1805.09501 Learning Data Augmentation Strategies for Object Detection - https://arxiv.org/abs/1906.11172 RandAugment: Practical automated data augmentation... - https://arxiv.org/abs/1909.13719 AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty - https://arxiv.org/abs/1912.02781 3-Augment: DeiT III: Revenge of the ViT - https://arxiv.org/abs/2204.07118 Hacked together by / Copyright 2019, Ross Wightman N)partial)DictListOptionalUnion)ImageImageOps ImageEnhance ImageChops ImageFilter.)rrg$@)translate_constimg_mean Resamplingc|jdt}t|ttfrt j |S|S)Nresample)pop_DEFAULT_INTERPOLATION isinstancelisttuplerandomchoice)kwargs interpolations a/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/timm/data/auto_augment.py_interpolationr 5s6JJz+ABM-$/}}]++ c\d|vrtdkr|jdt||d<y)N fillcolorrr)_PIL_VERrr )rs r_check_args_tfr'<s,fF!2 ;'/F:r!c ~t||j|jtjd|ddddffi|SNrr' transformsizerAFFINEimgfactorrs rshear_xr2Bs96 3==5<s rtranslate_y_absrA]s96 3==5<.transformtsA!' 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Consists of multiple sections separated by dashes ('-'). The first section defines the AutoAugment policy (one of 'v0', 'v0r', 'original', 'originalr'). While the remaining sections define other arguments * 'mstd' - float std deviation of magnitude noise applied hparams: Other hparams (kwargs) for the AutoAugmentation scheme Returns: A PyTorch compatible Transform Examples:: 'original-mstd0.5' results in AutoAugment with original policy, magnitude_std 0.5 -rr*N(\d.*)rmstdrz"Unknown AutoAugment config section)r)splitrers setdefaultrrirk) rurconfig policy_namerNcskeyval aa_policys rauto_augment_transformrJs$  c "F)K ABZF  ? XXi # r7Q; bq6S &=   c ; >> >5 ?$KAI y !!r!)rrrrrrrrr~rrrrrr)rrrrrrrrrrrrrrr)rrrr%rr*)rrrrrrrrrrrrrrg?g?)rrrrrrrrrrrrrrr transformsctt|j\}}tj|}|tj |z }||fSrG)rzipitemsnparraysum)rprobss r_get_weighted_transformsrsIS*"2"2"456J HHUOE BFF5M !E u r!rc`|dk(rtS|dk(rtS|dk(rtS|rtStS)Nweights3awrh)_RAND_WEIGHTED_0_RAND_WEIGHTED_3A_RAND_3A_RAND_INCREASING_TRANSFORMS_RAND_TRANSFORMS)r increasings rrand_augment_choicesrs8 y u}   t|*4 &J:JJr!rrc n|xst}|xst}|Dcgc]}t||||c}Scc}w)Nrrr)rrr)rrrrrs rrand_augment_opsrsL ))G//JKU WCG  49g ? WW W2c eZdZddZdZdZy) RandAugmentNc.||_||_||_yrG)ops num_layerschoice_weights)rrrrs rrzRandAugment.__init__s$,r!ctjj|j|j|j du|j }|D] }||} |S)N)replacert)rrrrrr)rr0rros rrzRandAugment.__call__s_ii HH OO''4/!!    BS'C  r!c|jjd|jdz}|jD] }|d|z } |dz }|S)Nz(n=, ops= r)rrrrrrros rrzRandAugment.__repr__sU ^^ $ $T__,=V'D D(( B D+ B  c  r!)rNrrHr!rrrs- r!rc$t}d}d}d}|jd}|ddk(sJ|dd}|D]}|jd rt|dd} |%| }(t jd |} t | dkrM| dd\} } | d k(r.t | } | d kDr t d } |jd| | dk(r|jdt| | dk(rt| sd}| dk(r t| }| dk(r t| }| dk(r t | }Jdt|trt||}n||rtnt}d} t|trt|\}} t!||||}t#||| S)a; Create a RandAugment transform Args: config_str (str): String defining configuration of random augmentation. Consists of multiple sections separated by dashes ('-'). The first section defines the specific variant of rand augment (currently only 'rand'). The remaining sections, not order specific determine * 'm' - integer magnitude of rand augment * 'n' - integer num layers (number of transform ops selected per image) * 'p' - float probability of applying each layer (default 0.5) * 'mstd' - float std deviation of magnitude noise applied, or uniform sampling if infinity (or > 100) * 'mmax' - set upper bound for magnitude to something other than default of _LEVEL_DENOM (10) * 'inc' - integer (bool), use augmentations that increase in severity with magnitude (default: 0) * 't' - str name of transform set to use hparams (dict): Other hparams (kwargs) for the RandAugmentation scheme Returns: A PyTorch compatible Transform Examples:: 'rand-m9-n3-mstd0.5' results in RandAugment with magnitude 9, num_layers 3, magnitude_std 0.5 'rand-mstd1-tweights' results in mag std 1.0, weighted transforms, default mag of 10 and num_layers 2 rFrrwrrandr*NtrxrydrrmmaxrincTmnrtz"Unknown RandAugment config section)r)rrrr)r)rrz startswithrsr{rsrr|rboolrrrrrrrr)rurrrrrrr}rNrrrryrra_opss rrand_augment_transformrs<IJJ D   c "F !9   ABZF  C << ae*C! )Q'B2w{"1vHCf}Sz#: 0DT N*d#%=j%I" N  gZd eF vz. IIr!) rrrrrrrrrrrrrc n|xst}|xst}|Dcgc]}t|d||c}Scc}w)Nrr)r_AUGMIX_TRANSFORMSr)rrrrs r augmix_opsr_sO ))G11J          rc6eZdZdZd dZdZdZdZdZdZ y) AugMixAugmenta AugMix Transform Adapted and improved from impl here: https://github.com/google-research/augmix/blob/master/imagenet.py From paper: 'AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty - https://arxiv.org/abs/1912.02781 cJ||_||_||_||_||_yrG)ralphawidthdepthblended)rrrrrrs rrzAugMixAugment.__init__ts%    r!c||z}d}g}|dddD] }||z }|d|z z}|j|"tj|dddtjS)Nrr*dtype)rprrfloat32)rwsrcumprwsrYrs r_calc_blended_weightsz#AugMixAugment._calc_blended_weights{sn !VDbD AHE QY D JJu  xxDbD 44r!cv|j}|j||}|D]}|jdkDr |jntjj dd}tjj |j|d}|} |D] } | | } tj|| |}|S)Nrr*rTr) rrrrrrandintrrrblend) rr0mixing_weightsrimg_origrrYrrimg_augros r_apply_blendedzAugMixAugment._apply_blendeds 88:  ' ' : /A"&**q.DJJbii6G6G16ME))""488UD"ACG &W+ &++c7A.C  / r!c|jd|jdt|jf}tj|tj }|D]}|j dkDr |j ntjjdd}tjj|j|d}|} |D] } | | } ||tj| tj zz }tj|dd|tj|jtj }tj"|||S) Nrr*rrTrgo@)out)r-rsgetbandsrzerosrrrrrrasarrayclipr fromarrayastypeuint8r) rr0rr img_shapemixedmwrrrros r _apply_basiczAugMixAugment._apply_basicsHHQK!c#,,..AA "**5  @B"&**q.DJJbii6G6G16ME))""488UD"ACG &W+ & R"**WBJJ?? ?E  @ q$E* RXX 67{{3q))r!ctjtjj|jg|j z}tjtjj |j|j}|jr|j|||}|S|j|||}|SrG) rrr dirichletrrbetarrr)rr0rrrs rrzAugMixAugment.__call__sBII$7$7 tzz8Q$RS JJryy~~djj$**= > <<''^Q?E %%c>1=E r!c|jjd|jd|jd|jdz}|j D] }|d|z } |dz }|S)Nz(alpha=z, width=z, depth=rrr)rrrrrrrs rrzAugMixAugment.__repr__sn ^^ $ $ HTZZLPXY]YcYcXddj'k k(( B D+ B  c  r!N)rrrF) rrr__doc__rrrrrrrHr!rrrns% 5 *"r!rc&d}d}d}d}d}|jd}|ddk(sJ|dd }|D]}tjd |} t| d kr(| d d \} } | d k(r|jd t | Q| dk(r t | }b| dk(r t | }s| dk(r t | }| dk(r t | }| dk(r t | }Jd|jd t dt||} t| ||||S)a  Create AugMix PyTorch transform Args: config_str (str): String defining configuration of random augmentation. Consists of multiple sections separated by dashes ('-'). The first section defines the specific variant of rand augment (currently only 'rand'). The remaining sections, not order specific determine 'm' - integer magnitude (severity) of augmentation mix (default: 3) 'w' - integer width of augmentation chain (default: 3) 'd' - integer depth of augmentation chain (-1 is random [1, 3], default: -1) 'b' - integer (bool), blend each branch of chain into end result without a final blend, less CPU (default: 0) 'mstd' - float std deviation of magnitude noise applied (default: 0) Ex 'augmix-m5-w4-d2' results in AugMix with severity 5, chain width 4, chain depth 2 hparams: Other hparams (kwargs) for the Augmentation transforms Returns: A PyTorch compatible Transform rrrFrwraugmixr*NrxrryrrrYrOrLrMzUnknown AugMix config sectionr)rr)rrrr) rzr{rsr|rrrrr) rurrrrrrr}rNrrrrs raugment_and_mix_transformrs1&I E E EG   c "F !9  ABZF : XXi # r7Q; bq6S &=   c ; CZCI CZHE CZHE CZ#JE CZ3iG 99 95':( e 5 y' :C EeW UUr!)r)rErT)rfNrG)T)rrNN)NN)rNN)arrrSr{ functoolsrtypingrrrrPILrr r r r numpyrr __version__rzrr&rrrrhasattrrBILINEARBICUBICrrr r'r2r4r:r<r?rArRrbrdrfrhrxr{rrrrrrrrrrrrrrrrrrrrrrrrrBrFr^r`rbrirkrsrrrrrrrrrrrrrrrr)rIs0rrs, ..FF  #//"7"7"& "*  1 6387 3 . / ,;08 = 8 D d "  ( =9&;- "7%: '!"<#$&%&<!!--00.SI0#sK,9 @M f f    9 (< Uu(* J!"#$%!!$$!*7 >//dBDBB .* "s "Xd^ "F(( *&KsK(*"&26 We$ W W$ WU4:./ W8#'7;QJQJ$QJU3d?34QJh$(*"&7; e$ $ U3d?34 FFR1V#1V1VkBsK